In search of an appropriate abstraction level for motif annotations

In: Proceedings of the 2012 Workshop on Computational Models of Narrative, (pp. 22-28).

Decision making in uncertain times: what can cognitive and decision sciences say about or learn from economic crises?

B.M. was supported by a Visiting Scholar Award from the British Academy and Grant ME 3717/2 from the Deutsche Forschungsgemeinschaft (DFG) as part of the priority program ‘New Frameworks of Rationality’ (SPP 1516)

Multisource energy conversion in plants with soft epicuticular coatings

Living plants have recently been exploited for unusual tasks such as energy conversion and environmental sensing. Yet, using plants as small-scale autonomous energy sources is often impeded by multicable and -electrode installations on the plants. Moreover, insufficient power outputs for steadily driving even low-power electronics made a realization challenging. Here, we show that plants, by a modification of the leaf epicuticular region can be transformed into cable-free, fully plant-enabled integrated devices for multisource energy conversion. In detail, leaf contact electrification caused by wind-induced inter-leaf tangency is magnified by a transparent elastomeric coating on one of two interacting leaves. This enables converting wind energy into harvestable electricity. Further, the same plant is used as an unmatched Marconi-antenna for multi-band radio frequency (RF) energy conversion. This enables the use of the same plant as a complementary multi-energy system with augmented power output if both sources are used simultaneously. In combination, we observed over 1000% enhanced energy accumulation respective to single source harvesting in the specific application case and common plants like ivy could power a commercial sensing platform wirelessly transmitting environmental data. This shows that living plants have potential to autonomously supply application-oriented electronics while maintaining the positive environmental impact by their intrinsic sustainability and benefits such as O-2 production, CO2 fixation, self-repair, and many more

Parallelizing an Index Generator for Desktop Search

International audienceExperience with the parallelization of an index generator for desktop search is presented. Several configurations of the index generator are compared on three different Intel platforms with 4, 8, and 32 cores. The optimal configurations for these platforms are not intuitive and are markedly different for the three platforms. For finding the optimal configuration, detailed measurements and experimentation were necessary. Several recommendations for parallel software design are derived from this study

Ergodicity-breaking reveals time optimal decision making in humans

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations

Data-driven gamification design

Gamification has been attracted much interest, not only in the HCI community, in the last few years. However, there is still a lack of insights and theory on the relationships between game design elements, motivation, domain context and user behavior. In this workshop we want to discover the potentials of data-driven gamification design optimization, e.g. by the application of machine learning techniques on user interaction data in a certain domain

Ultraconformable, Self‐Adhering Surface Electrodes for Measuring Electrical Signals in Plants

The electrical signals in plant's physiological processes are of great interest in biology, biohybrid robotics, and sensors for interfacing the living organisms with an electronic readout and control. This paper reports on the application of conformable, self-adhering surface electrodes for the measurement and bidirectional stimulation of electrical signals in plants. The inkjet-printed poly(3,4-ethylenedioxythiophene) polystyrene sulfonate based electrodes are <3 µm thick, light-weight, soft and flexible, and can be easily and non-invasively transferred onto plant's outer organs for surface potential recordings due to their realization on tattoo transfer paper. The devices prove to be extremely versatile for analyzing electrical signals in Dionaea muscipula, Arabidopsis thaliana, and Codariocalyx motorius and for stimulating mechanical responses in D. muscipula. A benefit over traditional electrodes is the van der Waals self-adherence of the thin electrodes, their intrinsic flexibility and adaptation also on small leaves while providing excellent readout. The same electrode allows long-term multicycle measurements over at least 10 days and, moreover, straightforward recordings on fast-moving organs such as snapping fly traps and endogenously oscillating leaflets. The results confirm that self-adhering soft organic electronics are particularly suitable for plant electrical signal analysis when easy-application, self-adaptation, and long-term performance are required in plant science, biohybrid robotics, and biohybrid sensors

A blood based 12-miRNA signature of Alzheimer disease patients

Background: Alzheimer disease (AD) is the most common form of dementia but the identification of reliable, early and non-invasive biomarkers remains a major challenge. We present a novel miRNA-based signature for detecting AD from blood samples. Results: We apply next-generation sequencing to miRNAs from blood samples of 48 AD patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression levels. Of these, 82 have higher and 58 have lower abundance in AD patient samples. We selected a panel of 12 miRNAs for an RT-qPCR analysis on a larger cohort of 202 samples, comprising not only AD patients and healthy controls but also patients with other CNS illnesses. These included mild cognitive impairment, which is assumed to represent a transitional period before the development of AD, as well as multiple sclerosis, Parkinson disease, major depression, bipolar disorder and schizophrenia. miRNA target enrichment analysis of the selected 12 miRNAs indicates an involvement of miRNAs in nervous system development, neuron projection, neuron projection development and neuron projection morphogenesis. Using this 12-miRNA signature, we differentiate between AD and controls with an accuracy of 93%, a specificity of 95% and a sensitivity of 92%. The differentiation of AD from other neurological diseases is possible with accuracies between 74% and 78%. The differentiation of the other CNS disorders from controls yields even higher accuracies. Conclusions: The data indicate that deregulated miRNAs in blood might be used as biomarkers in the diagnosis of AD or other neurological diseases